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Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0229253","usgsCitation":"Young, N.E., Jarnevich, C.S., Sofaer, H., Pearse, I.S., Sullivan, J., Engelstad, P., and Stohlgren, T.J., 2020, A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales: PLoS ONE, v. 15, no. 3, e0229253, 21 p., https://doi.org/10.1371/journal.pone.0229253.","productDescription":"e0229253, 21 p.","ipdsId":"IP-115209","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":457457,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0229253","text":"Publisher Index Page"},{"id":437068,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V54H5K","text":"USGS data release","linkHelpText":"INHABIT species potential distribution across the contiguous United States (ver. 3.0, February 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Island, Alabama","interactions":[],"lastModifiedDate":"2022-04-21T20:32:11.498263","indexId":"ofr20201003","displayToPublicDate":"2020-03-09T09:04:23","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1003","displayTitle":"Predicting Barrier Island Habitats and Oyster and Seagrass Habitat Suitability for Various Restoration Measures and Future Conditions for Dauphin Island, Alabama","title":"Predicting barrier island habitats and oyster and seagrass habitat suitability for various restoration measures and future conditions for Dauphin Island, Alabama","docAbstract":"

Barrier islands, such as Dauphin Island, Alabama, provide numerous invaluable ecosystem services including storm damage reduction and erosion control to the mainland, habitat for fish and wildlife, carbon sequestration in marshes, water catchment and purification, recreation, and tourism. These islands are dynamic environments that are gradually shaped by currents, waves, and tides under quiescent conditions yet can evolve in the time scale of hours to days during hurricanes and other extreme storms. The ecosystems associated with these islands also face numerous other hazards, including accelerated sea-level rise, oil spills, and anthropogenic stressors.

Hurricane Katrina in 2005 and the Deepwater Horizon oil spill in 2010 are two major events that have affected habitats and natural resources on Dauphin Island, Ala. The latter event prompted a cooperative effort between the U.S. Geological Survey and the U.S. Army Corps of Engineers to investigate viable, sustainable restoration measures that reduce degradation and enhance the natural resources of Dauphin Island, Ala. In collaboration with the State of Alabama and the National Fish and Wildlife Foundation, the overarching goal of the Alabama Barrier Island Restoration Feasibility Assessment project was to document baseline conditions and forecast potential conditions under varying sea-level change and storm scenarios for a no-action alternative along with a variety of restoration measures including beach and dune restoration, marsh and back-barrier restoration, and placement of sand in the littoral zone. The modeling component of this project used decadal hydrodynamic geomorphic, water quality, and habitat modeling to better understand how the various restoration measures may influence the habitat composition, sustainability, and resiliency of Dauphin Island under potential future conditions, benchmarked against the no-action case.

The report covers the habitat modeling efforts associated with the Alabama Barrier Island Restoration Feasibility Assessment project. For various potential future island configurations for Dauphin Island, we predicted coverage of habitat types (for example, beach, dune, intertidal marsh, and woody vegetation) using a spatially explicit habitat model based on landscape-position information (for example, elevation and distance from shore) extracted from the hydrodynamic geomorphic outputs. Similarly, we forecasted habitat suitability for oysters and seagrass using habitat suitability index models. Another component of the Alabama Barrier Island Restoration Feasibility Assessment project, presented separately, integrates these habitat model results into a structured decision-making framework that accounts for competing objectives. Collectively, this information provides insights to natural resource managers and planners on how a restoration measure may maintain or impede natural coastal processes and provide information critical for making future-focused decisions regarding barrier island restoration.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201003","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and in collaboration with the State of Alabama and the National Fish and Wildlife Foundation","usgsCitation":"Enwright, N.M., Wang, H., Dalyander, P.S., and Godsey, E., eds., 2020, Predicting barrier island habitats and oyster and seagrass habitat suitability for various restoration measures and future conditions for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1003, 99 p., https://doi.org/10.3133/ofr20201003.","productDescription":"Report: x, 99 p.; 3 Data Releases","numberOfPages":"114","onlineOnly":"Y","ipdsId":"IP-113342","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":372971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1003/ofr20201003.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1003"},{"id":372973,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O30XMZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Oyster habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":399449,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109756.htm"},{"id":372974,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B32VTE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Seagrass habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":372972,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PK0EH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Landscape position-based habitat modeling for the Alabama Barrier Island feasibility assessment at Dauphin Island"},{"id":372970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1003/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.341064453125,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.166500980766052\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-03-09","noUsgsAuthors":false,"publicationDate":"2020-03-09","publicationStatus":"PW","contributors":{"editors":[{"text":"Enwright, Nicholas M. 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":202150,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":784073,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":215073,"corporation":false,"usgs":false,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":784074,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":784075,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Godsey, Elizabeth 0000-0003-4621-7857","orcid":"https://orcid.org/0000-0003-4621-7857","contributorId":222094,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","affiliations":[{"id":34200,"text":"Army Corp of Engineers","active":true,"usgs":false}],"preferred":false,"id":784076,"contributorType":{"id":2,"text":"Editors"},"rank":4}]}} ,{"id":70228339,"text":"70228339 - 2020 - Estimating population abundance with a mixture of physical capture and passive PIT tag antenna detection data","interactions":[],"lastModifiedDate":"2022-02-09T18:32:50.128954","indexId":"70228339","displayToPublicDate":"2020-03-07T12:29:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population abundance with a mixture of physical capture and passive PIT tag antenna detection data","docAbstract":"

The inclusion of passive interrogation antenna (PIA) detection data has promise to increase precision of population abundance estimates (\"\"). However, encounter probabilities are often higher for PIAs than for physical capture. If the difference is not accounted for, \"\" may be biased. Using simulations, we estimated the magnitude of bias resulting from mixed capture and detection probabilities and evaluated potential solutions for removing the bias for closed capture models. Mixing physical capture and PIA detections (pdet) resulted in negative biases in \"\". However, using an individual covariate to model differences removed bias and improved precision. From a case study of fish making spawning migrations across a stream-wide PIA (pdet ≤ 0.9), the coefficient of variation (CV) of \"\" declined 39%–82% when PIA data were included, and there was a dramatic reduction in time to detect a significant change in \"\". For a second case study, with modest pdet (≤0.2) using smaller PIAs, CV (\"\") declined 4%–18%. Our method is applicable for estimating abundance for any situation where data are collected with methods having different capture–detection probabilities.

","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0326","usgsCitation":"Conner, M.M., Budy, P., Wilkison, R.A., Mills, M., Speas, D., Mackinnon, P.D., and Mark C. Mckinstry, 2020, Estimating population abundance with a mixture of physical capture and passive PIT tag antenna detection data: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 7, p. 1163-1171, https://doi.org/10.1139/cjfas-2019-0326.","productDescription":"9 p.","startPage":"1163","endPage":"1171","ipdsId":"IP-110596","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2019-0326","text":"Publisher Index Page"},{"id":395707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Conner, Mary M.","contributorId":275216,"corporation":false,"usgs":false,"family":"Conner","given":"Mary","email":"","middleInitial":"M.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkison, Richard A.","contributorId":275217,"corporation":false,"usgs":false,"family":"Wilkison","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":56749,"text":"ipc","active":true,"usgs":false}],"preferred":false,"id":833838,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mills, Michael","contributorId":275218,"corporation":false,"usgs":false,"family":"Mills","given":"Michael","email":"","affiliations":[{"id":56750,"text":"uwc","active":true,"usgs":false}],"preferred":false,"id":833839,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Speas, David","contributorId":275219,"corporation":false,"usgs":false,"family":"Speas","given":"David","email":"","affiliations":[{"id":56751,"text":"ubr","active":true,"usgs":false}],"preferred":false,"id":833840,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mackinnon, Peter D.","contributorId":275220,"corporation":false,"usgs":false,"family":"Mackinnon","given":"Peter","email":"","middleInitial":"D.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833841,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mark C. Mckinstry","contributorId":275221,"corporation":false,"usgs":false,"family":"Mark C. Mckinstry","affiliations":[{"id":56751,"text":"ubr","active":true,"usgs":false}],"preferred":false,"id":833842,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209001,"text":"70209001 - 2020 - Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife","interactions":[],"lastModifiedDate":"2020-03-10T19:28:47","indexId":"70209001","displayToPublicDate":"2020-03-06T19:21:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife","docAbstract":"Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3046","usgsCitation":"Katzner, T., Braham, M.A., Conkling, T., Diffendorfer, J., Duerr, A.E., Loss, S., Nelson, D.M., Vander Zanden, H.B., and Yee, J.L., 2020, Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife: Ecosphere, v. 11, no. 3, e03046, 23 p., https://doi.org/10.1002/ecs2.3046.","productDescription":"e03046, 23 p.","ipdsId":"IP-108403","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3046","text":"Publisher Index Page"},{"id":373085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Guatemala, Haiti, Honduras, Jamaica, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.89453125,\n 17.97873309555617\n ],\n [\n -75.76171875,\n 21.779905342529645\n ],\n [\n -81.5625,\n 30.751277776257812\n ],\n [\n -74.53125,\n 35.17380831799959\n ],\n [\n -71.3671875,\n 39.90973623453719\n ],\n [\n -58.35937499999999,\n 45.460130637921004\n ],\n [\n -51.67968749999999,\n 46.6795944656402\n ],\n [\n -55.8984375,\n 53.74871079689897\n ],\n [\n -63.45703124999999,\n 58.81374171570782\n ],\n [\n -78.22265625,\n 52.696361078274485\n ],\n [\n -80.15625,\n 51.069016659603896\n ],\n [\n -82.265625,\n 53.64463782485651\n ],\n [\n -99.66796875,\n 58.35563036280964\n ],\n [\n 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]\n}","volume":"11","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braham, Melissa A.","contributorId":199740,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":34303,"text":"West Virginia University, Department of Geology & Geography","active":true,"usgs":false}],"preferred":false,"id":784470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conkling, Tara 0000-0003-1926-8106","orcid":"https://orcid.org/0000-0003-1926-8106","contributorId":217915,"corporation":false,"usgs":true,"family":"Conkling","given":"Tara","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":784472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":784473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, Scott R.","contributorId":140471,"corporation":false,"usgs":false,"family":"Loss","given":"Scott R.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":784474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, David M.","contributorId":175098,"corporation":false,"usgs":false,"family":"Nelson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":784476,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vander Zanden, Hannah B.","contributorId":138885,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"Hannah","email":"","middleInitial":"B.","affiliations":[{"id":12562,"text":"Department of Geology and Geophysics, University of Utah; Archie Carr Center for Sea Turtle Research, University of Florida","active":true,"usgs":false}],"preferred":false,"id":784475,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":784477,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227752,"text":"70227752 - 2020 - A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin","interactions":[],"lastModifiedDate":"2022-04-15T16:17:24.763904","indexId":"70227752","displayToPublicDate":"2020-03-06T11:02:45","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9366,"text":"CCAST Case Study on Actionable Science","active":true,"publicationSubtype":{"id":1}},"title":"A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin","docAbstract":"

Management of water resources in the transboundary Rio Grande/Río Bravo Basin (the Basin) presents challenges for state and Federal entities in the United States and Mexico making management decisions on shared water resources. Damming, channelization, water availability, and allocation are governed by water rights and water-sharing agreements. Data and information sharing are important aspects of transboundary cooperation, but differences in format, content, spatial and temporal resolution, and language hinder collaboration. In addition, data on the kinds and geographic distribution of water governance and management institutions across the Basin have not been consistently documented. Existing data disparities parallel the hydrological and social fragmentation of the Basin.

Seeking to underscore the interdependence between social and environmental processes in the Basin, anthropologists and modelers collaborated to develop a socio-environmental geodatabase. This geodatabase is a first step in modeling the social components of decision making and their connectivity to environmental processes across the Basin. The geodatabase is available in an open-access domain and contains geospatial data related to water and land governance, hydrology, water use and hydraulic infrastructure, socioeconomics, and the biophysical environment necessary to advance the understanding of basin dynamics. Having these data documented and compiled in a central location serves as a resource to help decision makers better understand upstream and downstream social-environmental characteristics. This knowledge is useful for developing sustainable water management policies in a region where water resources are increasingly under pressure from climatic, environmental, and human-related changes.

","language":"English","publisher":"Collaborative Conservation and Adaptation Strategy Toolbox (CCAST)","usgsCitation":"Villa, J., 2020, A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin: CCAST Case Study on Actionable Science, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-123961","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":398832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395033,"type":{"id":15,"text":"Index Page"},"url":"https://arcg.is/0bava9"}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","otherGeospatial":"Rio Grande/Río Bravo basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.12493896484374,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 31.421631960419596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Villa, Jennifer 0000-0002-4774-7166","orcid":"https://orcid.org/0000-0002-4774-7166","contributorId":245824,"corporation":false,"usgs":true,"family":"Villa","given":"Jennifer","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832043,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70212501,"text":"70212501 - 2020 - Evidence for late Quaternary deformation along Crowley's Ridge, New Madrid seismic zone","interactions":[],"lastModifiedDate":"2020-08-18T14:24:18.557686","indexId":"70212501","displayToPublicDate":"2020-03-06T09:19:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for late Quaternary deformation along Crowley's Ridge, New Madrid seismic zone","docAbstract":"

The New Madrid seismic zone has been the source of multiple major (M ~7.0–7.5) earthquakes in the past 2 ka, yet the surface expression of recent deformation remains ambiguous. Crowleys Ridge, a linear ridge trending north‐south for 300+ km through the Mississippi Embayment, has been interpreted as either a fault‐bounded uplift or a nontectonic erosional remnant. New and previously published seismic reflection and shallow resistivity data show discontinuities at the ridge margins in Plio‐Pleistocene strata, yet the timing of most recent faulting and the lateral extent of these faults remain unknown. To assess Pleistocene‐to‐recent tectonic activity of Crowleys Ridge, we perform landscape‐scale geomorphic analyses, such as relief, slope, hypsometry, and drainage basin shape, on a 10‐m digital elevation model (DEM). North‐to‐south variations in geomorphic indices indicate Pleistocene‐to‐recent tectonic uplift of the southern ridge. Moreover, mapping on a <1‐m lidar‐derived DEM reveals scarps on late Pleistocene geomorphic surfaces. The scarps are primarily located along the southern ridge, trend parallel to the ridge margin discontinuously for 0.1–1 km, and vertically offset <56 ka surfaces 0.4 m with up to 6 m of tilting. These landscape‐scale patterns and scarps, integrated with discontinuities in the seismic reflection and resistivity data, provide evidence of low‐rate (<0.2 mm/year) late Quaternary tectonic activity along the southern segment of Crowleys Ridge. The interpretations agree with recent tectonic models suggesting southern Crowleys Ridge is a compressional step over in a right‐lateral fault system within the Reelfoot Rift.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019TC005746","usgsCitation":"Thompson Jobe, J., Gold, R.D., Briggs, R.W., Williams, R., Stephenson, W.J., Delano, J.E., Shah, A.K., and Minsley, B.J., 2020, Evidence for late Quaternary deformation along Crowley's Ridge, New Madrid seismic zone: Tectonics, v. 39, e2019TC005746, 30 p., https://doi.org/10.1029/2019TC005746.","productDescription":"e2019TC005746, 30 p.","ipdsId":"IP-114068","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437069,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TFRP5D","text":"USGS data release","linkHelpText":"Digital datasets documenting subsurface data locations, topographic metrics, fault scarp mapping, and revised fault network for Crowley's Ridge, New Madrid Seismic Zone"},{"id":377600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Missouri, Tennessee","otherGeospatial":"Crowley's Ridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.724853515625,\n 34.551811369170494\n ],\n [\n -87.989501953125,\n 34.551811369170494\n ],\n [\n -87.989501953125,\n 37.57070524233116\n ],\n [\n -91.724853515625,\n 37.57070524233116\n ],\n [\n -91.724853515625,\n 34.551811369170494\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2020-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":225113,"corporation":false,"usgs":false,"family":"Thompson Jobe","given":"Jessica","email":"","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":796600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Robert 0000-0002-2973-8493 rawilliams@usgs.gov","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":140741,"corporation":false,"usgs":true,"family":"Williams","given":"Robert","email":"rawilliams@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delano, Jaime E. 0000-0003-2601-2600","orcid":"https://orcid.org/0000-0003-2601-2600","contributorId":210604,"corporation":false,"usgs":true,"family":"Delano","given":"Jaime","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":796606,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":796607,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209827,"text":"70209827 - 2020 - Disease can shape marine ecosystems","interactions":[],"lastModifiedDate":"2020-06-04T17:38:13.96276","indexId":"70209827","displayToPublicDate":"2020-03-06T09:12:30","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Disease can shape marine ecosystems","docAbstract":"

This chapter reviews how marine ecosystems respond to parasites. Evidence from several marine ecosystems shows that parasites can wield control over ecosystem structure, function, and dynamics by regulating host density and phenotype. Like predators, parasites can generate or modify trophic cascades, regulate important foundational species and ecosystem engineers, and mediate species coexistence by affecting competitive outcomes. Sometimes the parasites have clear positive impacts within ecosystems, such as increasing species diversity or maintaining ecosystem stability. Other times, parasites may have destabilizing effects that signal an ecosystem out of balance. But it is now clear that some (but not all) parasites can have strong and, at times, predictable effects, and should thus be incorporated into food web and ecosystem models.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Marine disease ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/oso/9780198821632.003.0003","collaboration":"NPS","usgsCitation":"Morton, J.P., Silliman, B.R., and Lafferty, K.D., 2020, Disease can shape marine ecosystems, chap. 3 of Marine disease ecology, p. 61-70, https://doi.org/10.1093/oso/9780198821632.003.0003.","productDescription":"10 p.","startPage":"61","endPage":"70","ipdsId":"IP-104758","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":375351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-05-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Morton, Joseph P","contributorId":224405,"corporation":false,"usgs":false,"family":"Morton","given":"Joseph","email":"","middleInitial":"P","affiliations":[],"preferred":false,"id":788193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silliman, Brian R","contributorId":221797,"corporation":false,"usgs":false,"family":"Silliman","given":"Brian","email":"","middleInitial":"R","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":788194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":788195,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223324,"text":"70223324 - 2020 - Geodetic measurements of slow slip events southeast of Parkfield, CA","interactions":[],"lastModifiedDate":"2021-08-23T23:00:40.643575","indexId":"70223324","displayToPublicDate":"2020-03-05T17:54:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Geodetic measurements of slow slip events southeast of Parkfield, CA","docAbstract":"

Tremor and low-frequency earthquakes are presumed to be indicative of surrounding slow, aseismic slip that is often below geodetic detection thresholds. This study uses data from borehole seismometers and long-baseline laser strainmeters to observe both the seismic and geodetic signatures of episodic tremor and slip on the Parkfield region of the San Andreas Fault near Cholame, CA. The observed occurrence rates of both the tremors and co-located families of low-frequency earthquakes are not steady but instead exhibit quasiperiodic bursts of increased activity. We show that these periods of elevated seismic activity correlate with statistically significant stacked strain signals consisting of 44 slow-slip events. Modeled individual slow-slip events and their total summed moment, which are constrained by seismic signals and stacked strain, respectively, indicate that the individual moment magnitudes of these events range from \"urn:x-wiley:jgrb:media:jgrb54084:jgrb54084-math-0001\" 4.6–5.2. We find that the measured geodetic signal likely precedes the seismic signal by several hours, consistent with the aseismic slip preceding and driving the observed seismic tremor activity. We confirm that strike-slip faults, in addition to subduction zones, are capable of producing episodic tremor and slip.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB019059","usgsCitation":"Delbridge, B.G., Carmichael, J.D., Nadeau, R., Shelly, D.R., and Burgmann, R., 2020, Geodetic measurements of slow slip events southeast of Parkfield, CA: Journal of Geophysical Research, v. 125, no. 5, e2019JB019059, 20 p., https://doi.org/10.1029/2019JB019059.","productDescription":"e2019JB019059, 20 p.","ipdsId":"IP-117218","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":457485,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1630866","text":"External Repository"},{"id":388397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Parkfield slow-slip region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.640625,\n 35.15584570226544\n ],\n [\n -118.27880859374999,\n 35.15584570226544\n ],\n [\n -118.27880859374999,\n 36.66841891894786\n ],\n [\n -121.640625,\n 36.66841891894786\n ],\n [\n -121.640625,\n 35.15584570226544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Delbridge, Brent G. 0000-0003-2808-8772","orcid":"https://orcid.org/0000-0003-2808-8772","contributorId":192986,"corporation":false,"usgs":false,"family":"Delbridge","given":"Brent","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":821739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carmichael, Joshua D. 0000-0001-5752-5738","orcid":"https://orcid.org/0000-0001-5752-5738","contributorId":264608,"corporation":false,"usgs":false,"family":"Carmichael","given":"Joshua","email":"","middleInitial":"D.","affiliations":[{"id":54513,"text":"EES-17 (Geophysics), Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":821740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nadeau, Robert M. 0000-0003-1255-0643","orcid":"https://orcid.org/0000-0003-1255-0643","contributorId":264609,"corporation":false,"usgs":false,"family":"Nadeau","given":"Robert M.","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":821741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":821742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burgmann, Roland 0000-0002-3560-044X","orcid":"https://orcid.org/0000-0002-3560-044X","contributorId":264610,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","email":"","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":821743,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206596,"text":"pp1863 - 2020 - Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","interactions":[],"lastModifiedDate":"2022-04-22T19:10:54.810814","indexId":"pp1863","displayToPublicDate":"2020-03-05T09:14:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1863","displayTitle":"Groundwater Characterization and Effects of Pumping in the Death Valley Regional Groundwater Flow System, Nevada and California, with Special Reference to Devils Hole","title":"Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","docAbstract":"

Groundwater flow and development were characterized in four groundwater basins of the Death Valley regional flow system in Nevada and California with calibrated, groundwater-flow models. Natural groundwater discharges in the Furnace Creek, Lower Amargosa, and Saratoga Spring areas were defined and distributed consistently with a revised hydrogeologic framework. This simplified hydrogeologic framework was limited to four hydraulically unique, hydrogeologic units: (1) basin fill; (2) carbonate rocks; (3) volcanic rocks; and (4) low-permeability granitic and siliciclastic rocks. Hydrogeologic units and division of carbonate and volcanic rocks between shallow and deep were supported by results from 271 aquifer tests and specific-capacity estimates. Greater than 90 percent of field-estimated transmissivity occurred within 1,600 feet (ft) of the water table. Pumping in the study area from 1960 to 2010 averaged 46,000 acre-feet per year (acre-ft/yr), which is 80 percent of the predevelopment discharge. The central Amargosa Desert and Pahrump Valley were the two primary pumping centers and measurably affected water levels across 900 square miles in 2018.

Water levels in Devils Hole were a special focus because endangered Devils Hole pupfish (Cyprinodon diabolis) are affected by water-level declines. Pumping 42,100 acre-ft by Cappaert Enterprises, formerly Spring Meadows, Inc., caused a 2.3-ft water-level decline in Devils Hole, which temporarily reduced habitat of Devils Hole pupfish by 85 percent in 1972. If no pumping occurred, water levels in Devils Hole would have risen naturally about 1 ft between 1973 and 2018 from temporal variations in recharge. The 2.6-ft range of measured water-level changes in Devils Hole was simulated with a root-mean-square error of 0.2 ft during the 70-year period of record. Simulated water-level declines from pumping totaled 1.4 ft in 2018, with 25 and 34 percent attributed to pumping by Cappaert Enterprises and the central Amargosa Desert, respectively. Water levels in Devils Hole will decline at rates of 0.1–0.2 ft per decade if pumping from Ash Meadows groundwater basin and the central Amargosa Desert continue at current rates. Effects of future natural water-level fluctuations remain unknown.

Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins are hydraulically connected near well AD-4, about 5 miles south of the town of Amargosa Valley, Nevada. About 40 percent of the discharge from the Furnace Creek area is recharged in the Ash Meadows groundwater basin. Basin fill in the central Amargosa Desert hydraulically connects carbonate rocks east of well AD-4 with saturated carbonate rocks in the Funeral Range. About 7 percent of the 960,000 acre-ft pumped from Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins prior to 2019 was captured discharge from springs and phreatophytes. Greater than 40 percent of the 2,080,000 acre-ft pumped from Pahrump Valley between 1910 and 2019 was capture that primarily discharged from Bennetts and Manse Springs.

Simulated advective-flow distances and velocities from underground nuclear tests are within the range of advective transport calculations from tritium data and previous radionuclide transport investigations. Boundary conditions and flow rates from the regional model in this study are plausible for local-scale flow and radionuclide transport models. Simulated 165-year groundwater-flow paths do not extend into pumping areas and effects of regional pumping on advective transport are negligible.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1863","collaboration":"Prepared in cooperation with the U.S. Department of Energy Office of Environmental Management, National Nuclear Security Administration, Nevada Site Office, under Interagency Agreement DE-EM0004969","usgsCitation":"Halford, K.J., and Jackson, T.R., 2020, Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole: U.S. Geological Survey Professional Paper 1863, 178 p., https://doi.org/10.3133/pp1863.","productDescription":"Report: xvi, 178 p.; Data Release","ipdsId":"IP-105994","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":372815,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"},{"id":399508,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109738.htm"},{"id":372814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1863/pp1863.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1863"},{"id":372813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1863/coverthb2.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley, Devils Hole","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35.6464\n ],\n [\n -115.0611,\n 35.6464\n ],\n [\n -115.0611,\n 37.7214\n ],\n [\n -117,\n 37.7214\n ],\n [\n -117,\n 35.6464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-03-05","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Tracie R. 0000-0001-8553-0323 tjackson@usgs.gov","orcid":"https://orcid.org/0000-0001-8553-0323","contributorId":150591,"corporation":false,"usgs":true,"family":"Jackson","given":"Tracie","email":"tjackson@usgs.gov","middleInitial":"R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":775092,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209460,"text":"70209460 - 2020 - Biogeography of fire regimes in western US conifer forests: A trait-based approach","interactions":[],"lastModifiedDate":"2020-04-09T13:15:04.84918","indexId":"70209460","displayToPublicDate":"2020-03-05T08:05:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography of fire regimes in western US conifer forests: A trait-based approach","docAbstract":"Aim\nFunctional traits are a critical link between species distributions and the ecosystem processes that structure those species’ niches. Concurrent increases in the availability of functional trait data and our ability to model species distributions present an opportunity to develop functional trait biogeography, i.e. the mapping of functional traits across space. Functional trait biogeography can improve process-based predictions about the resistance of certain species assemblages to changing environmental conditions across landscape scales. We illustrate this concept by developing the first trait-based, quantitative ranking of fire resistance (adult tree survival) in North American conifer species, and mapping that fire resistance across space. \nLocation and Time period\nWestern Continental United States, present-day.\nMajor taxa studied\n29 common conifer tree species.\nMethods\nWe compiled six traits for each species: three relating to tree morphology and three relating to litter flammability. We combined these traits into a single fire resistance score, and used community-weighted averaging to estimate the fire resistance scores of different forest communities, using interpolated species distribution and relative abundance data.\nResults \nSpecies associated with historically frequent fire have high fire resistance scores (e.g., Pinus ponderosa), reflected by thick bark, tall crowns, and flammable litter. Species associated with subalpine or arid conditions have low fire resistance scores (e.g., Picea engelmannii and Pinus edulis), reflected by thin bark, short stature, poor self-pruning and low litter flammability. A map of forest community fire resistance across the western US reveals agreement with independent assessments of historical fire regimes, while also identifying areas where community-wide species traits may be mismatched with historical fire regimes. \nMain conclusions\nQuantifying the functional traits that confer resistance to tree-killing fire provides a direct link between ecosystem disturbance and community resistance. Understanding this link is critical to evaluating long-term resilience of different forest types under dynamic fire regimes. Our work represents the first known spatial representation of fire-resistance traits at a regional scale, and as such provides a link between functional traits and biogeography relevant to a critical ecosystem process.","language":"English","publisher":"Wiley","doi":"10.1111/geb.13079","collaboration":"","usgsCitation":"Stevens, J., Kling, M.M., Schwilk, D.W., Varner, J.M., and Kane, J., 2020, Biogeography of fire regimes in western US conifer forests: A trait-based approach: Global Ecology and Biogeography, v. 29, no. 5, p. 944-955, https://doi.org/10.1111/geb.13079.","productDescription":"12 p.","startPage":"944","endPage":"955","ipdsId":"IP-114014","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437071,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97F5P7L","text":"USGS data release","linkHelpText":"Fire resistance trait data for 29 western North American conifer species"},{"id":373858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"","otherGeospatial":"Western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 30.600093873550072\n ],\n [\n -103.53515625,\n 30.600093873550072\n ],\n [\n -103.53515625,\n 49.49667452747045\n ],\n [\n -125.5078125,\n 49.49667452747045\n ],\n [\n -125.5078125,\n 30.600093873550072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Jens 0000-0002-2234-1960","orcid":"https://orcid.org/0000-0002-2234-1960","contributorId":222191,"corporation":false,"usgs":true,"family":"Stevens","given":"Jens","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":786562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kling, Matthew M.","contributorId":223923,"corporation":false,"usgs":false,"family":"Kling","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":786630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwilk, Dylan W.","contributorId":103883,"corporation":false,"usgs":true,"family":"Schwilk","given":"Dylan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":786631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varner, J. Morgan","contributorId":197482,"corporation":false,"usgs":false,"family":"Varner","given":"J.","email":"","middleInitial":"Morgan","affiliations":[],"preferred":false,"id":786632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kane, Jeffrey M.","contributorId":35169,"corporation":false,"usgs":true,"family":"Kane","given":"Jeffrey M.","affiliations":[],"preferred":false,"id":786633,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249715,"text":"70249715 - 2020 - Fundamental hydraulics of cross sections in natural rivers: Preliminary analysis of a large data set of acoustic doppler flow measurements","interactions":[],"lastModifiedDate":"2023-10-25T12:14:01.11108","indexId":"70249715","displayToPublicDate":"2020-03-05T07:07:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11438,"text":"Water Resource Research","active":true,"publicationSubtype":{"id":10}},"title":"Fundamental hydraulics of cross sections in natural rivers: Preliminary analysis of a large data set of acoustic doppler flow measurements","docAbstract":"

We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models.

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Despite consensus that impervious surfaces increase flooding, the magnitude of the increase remains uncertain. This uncertainty largely stems from the challenge of isolating the effect of changes in impervious cover separate from other factors that also affect flooding. To control for these factors, prior study designs rely on either temporal or spatial variation in impervious cover. We leverage both temporal and spatial variation in a panel data regression design to isolate the effect of impervious cover on floods. With 39 years of data from 280 U.S. streamgages, we estimate that a one percentage point increase in impervious basin cover causes a 3.3% increase in annual flood magnitude (95%CI: 1.9%, 4.7%) on average. Using 2,109 streamgages, some of which have upstream regulation and/or overlapping basins, we estimate a larger effect: 4.6% (CI: 3.5%, 5.6%). The approach introduced here can be extended to estimate the causal effects of other drivers of hydrologic change.

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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5012","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Williston Basin, North Dakota and Montana, 2007–17","title":"Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17","docAbstract":"

This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use.

Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205012","collaboration":"Water Availability and Use Science Program","usgsCitation":"McShane, R.R., Barnhart, T.B., Valder, J.F., Haines, S.S., Macek-Rowland, K.M., Carter, J.M., Delzer, G.C., and Thamke, J.N., 2020, Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17: U.S. Geological Survey Scientific Investigations Report 2020–5012, 26 p., https://doi.org/10.3133/sir20205012","productDescription":"Report: vii, 26 p.; 2 Appendixes; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112448","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109737.htm"},{"id":372867,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 2","linkHelpText":"– Water-Use Estimates and Coefficients"},{"id":372866,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 1","linkHelpText":"– R Scripts"},{"id":372864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5012/coverthb2.jpg"},{"id":372868,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to Estimate Water Use Associated with Continuous Oil and Gas Development, Williston Basin, United States, 1980-2017 (ver. 2.0, September 2019)"},{"id":372865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5012"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8333,\n 44.8333\n ],\n [\n -99,\n 44.8333\n ],\n [\n -99,\n 49\n ],\n [\n -106.8333,\n 49\n ],\n [\n -106.8333,\n 44.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-03-04","noUsgsAuthors":false,"publicationDate":"2020-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":219010,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore","email":"","middleInitial":"B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valder, Joshua F. 0000-0003-3733-8868","orcid":"https://orcid.org/0000-0003-3733-8868","contributorId":220912,"corporation":false,"usgs":true,"family":"Valder","given":"Joshua F.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":782096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Macek-Rowland, Kathleen M. 0000-0003-2526-6860","orcid":"https://orcid.org/0000-0003-2526-6860","contributorId":219012,"corporation":false,"usgs":true,"family":"Macek-Rowland","given":"Kathleen M. ","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782097,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Janet M. 0000-0002-6376-3473","orcid":"https://orcid.org/0000-0002-6376-3473","contributorId":40660,"corporation":false,"usgs":true,"family":"Carter","given":"Janet M.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782098,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delzer, Gregory C. 0000-0002-7077-4963","orcid":"https://orcid.org/0000-0002-7077-4963","contributorId":203448,"corporation":false,"usgs":true,"family":"Delzer","given":"Gregory","email":"","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782099,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thamke, Joanna N. 0000-0002-6917-1946 jothamke@usgs.gov","orcid":"https://orcid.org/0000-0002-6917-1946","contributorId":1012,"corporation":false,"usgs":true,"family":"Thamke","given":"Joanna N.","email":"jothamke@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":782100,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227658,"text":"70227658 - 2020 - The changing sociocultural context of wildlife conservation","interactions":[],"lastModifiedDate":"2022-01-25T13:13:03.956979","indexId":"70227658","displayToPublicDate":"2020-03-04T07:09:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The changing sociocultural context of wildlife conservation","docAbstract":"

We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13493","usgsCitation":"Manfredo, M.J., Teel, T., Don Carlos, A., Sullivan, L., Bright, A.D., Dietsch, A., Bruskotter, J., and Fulton, D.C., 2020, The changing sociocultural context of wildlife conservation: Conservation Biology, v. 34, no. 6, p. 1549-1559, https://doi.org/10.1111/cobi.13493.","productDescription":"11 p.","startPage":"1549","endPage":"1559","ipdsId":"IP-108438","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457508,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.13493","text":"Publisher Index Page"},{"id":394814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Manfredo, Michael J.","contributorId":272146,"corporation":false,"usgs":false,"family":"Manfredo","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":831590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teel, Tara L.","contributorId":272147,"corporation":false,"usgs":false,"family":"Teel","given":"Tara L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":831591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Don Carlos, Andrew W.","contributorId":272148,"corporation":false,"usgs":false,"family":"Don Carlos","given":"Andrew W.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":831592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Leeann","contributorId":272149,"corporation":false,"usgs":false,"family":"Sullivan","given":"Leeann","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":831593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bright, Alan D.","contributorId":272150,"corporation":false,"usgs":false,"family":"Bright","given":"Alan","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":831594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dietsch, Alia M.","contributorId":272151,"corporation":false,"usgs":false,"family":"Dietsch","given":"Alia M.","affiliations":[{"id":56360,"text":"Ohio Sate University","active":true,"usgs":false}],"preferred":false,"id":831595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bruskotter, Jeremy","contributorId":272152,"corporation":false,"usgs":false,"family":"Bruskotter","given":"Jeremy","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":831596,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831589,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209065,"text":"70209065 - 2020 - Operational earthquake forecasting during the 2019 Ridgecrest, California, earthquake sequence with the UCERF3-ETAS model","interactions":[],"lastModifiedDate":"2020-05-05T17:11:43.076529","indexId":"70209065","displayToPublicDate":"2020-03-04T06:59:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Operational earthquake forecasting during the 2019 Ridgecrest, California, earthquake sequence with the UCERF3-ETAS model","docAbstract":"The first Uniform California Earthquake Rupture Forecast, Version 3–epidemic‐type aftershock sequence (UCERF3‐ETAS) aftershock simulations were running on a high‐performance computing cluster within 33 min of the 4 July 2019 M 6.4 Searles Valley earthquake. UCERF3‐ETAS, an extension of the third Uniform California Earthquake Rupture Forecast (UCERF3), is the first comprehensive, fault‐based, epidemic‐type aftershock sequence (ETAS) model. It produces ensembles of synthetic aftershock sequences both on and off explicitly modeled UCERF3 faults to answer a key question repeatedly asked during the Ridgecrest sequence: What are the chances that the earthquake that just occurred will turn out to be the foreshock of an even bigger event?","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190294","usgsCitation":"Milner, K.R., Field, E., Savran, W.H., Page, M.T., and Jordan, T.H., 2020, Operational earthquake forecasting during the 2019 Ridgecrest, California, earthquake sequence with the UCERF3-ETAS model: Seismological Research Letters, v. 91, no. 3, p. 1567-1578, https://doi.org/10.1785/0220190294.","productDescription":"12 p.","startPage":"1567","endPage":"1578","ipdsId":"IP-116016","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.81689453125,\n 33.8339199536547\n ],\n [\n -116.15844726562501,\n 33.8339199536547\n ],\n [\n -116.15844726562501,\n 36.62434536776987\n ],\n [\n -119.81689453125,\n 36.62434536776987\n ],\n [\n -119.81689453125,\n 33.8339199536547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":784689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":784688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savran, William H","contributorId":223256,"corporation":false,"usgs":false,"family":"Savran","given":"William","email":"","middleInitial":"H","affiliations":[],"preferred":false,"id":784690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":784691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jordan, Thomas H","contributorId":194144,"corporation":false,"usgs":false,"family":"Jordan","given":"Thomas","email":"","middleInitial":"H","affiliations":[],"preferred":false,"id":784692,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208886,"text":"70208886 - 2020 - Changing suspended sediment in United States rivers and streams: Linking sediment trends to changes in land use/cover, hydrology and climate","interactions":[],"lastModifiedDate":"2020-03-04T15:26:04","indexId":"70208886","displayToPublicDate":"2020-03-03T15:25:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Changing suspended sediment in United States rivers and streams: Linking sediment trends to changes in land use/cover, hydrology and climate","docAbstract":"

Sediment is one of the leading pollutants in rivers and streams across the United States (US) and the world. Between 1992 and 2012, concentrations of annual mean suspended sediment decreased at over half of the 137 stream sites assessed across the contiguous US. Increases occurred at less than 25 % of the sites, and the direction of change was uncertain at the remaining 25 %. Sediment trends were characterized using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model, and decreases in sediment ranged from −95 % to −8.5 % of the 1992 concentration. To explore potential drivers of these changes, the sediment trends were (1) parsed into two broad contributors of change, changes in land management versus changes in the streamflow regime, and (2) grouped by land use of the watershed and correlated to concurrent changes in land use or land cover (land use/cover), hydrology and climate variables and static/long-term watershed characteristics. At 83 % of the sites, changes in land management (captured by changes in the concentration–streamflow relationship over time; C–Q relationship) contributed more to the change in the sediment trend than changes in the streamflow regime alone (i.e., any systematic change in the magnitude, frequency or timing of flows). However, at >50 % of the sites, changes in the streamflow regime contributed at least a 5 % change in sediment, and at 11 sites changes in the streamflow regime contributed over half the change in sediment, indicating that at many sites changes in streamflow were not the main driver of changes in sediment but were often an important supporting factor. Correlations between sediment trends and concurrent changes in land use/cover, hydrology and climate were often stronger at sites draining watersheds with more homogenous, human-related land uses (i.e., agricultural and urban lands) compared to mixed-use or undeveloped lands. At many sites, decreases in sediment occurred despite small-to-moderate increases in the amount of urban or agricultural land in the watershed, suggesting conservation efforts and best-management practices (BMPs) used to reduce sediment runoff to streams may be successful, up to a point, as lands are converted to urban and agricultural uses.

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Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783837,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210746,"text":"70210746 - 2020 - Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","interactions":[],"lastModifiedDate":"2020-06-23T14:52:36.144242","indexId":"70210746","displayToPublicDate":"2020-03-03T09:48:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","docAbstract":"

Sediment contamination of freshwater streams in urban areas is a recognized and growing concern. As a part of a comprehensive regional stream‐quality assessment, stream‐bed sediment was sampled from streams spanning a gradient of urban intensity in the Piedmont ecoregion of the southeastern United States. We evaluated relations between a broad suite of sediment contaminants (metals, current‐use pesticides, organochlorine pesticides, polychlorinated biphenyls, brominated diphenyl ethers, and polycyclic aromatic hydrocarbons), ambient sediment toxicity, and macroinvertebrate communities from 76 sites. Sediment toxicity was evaluated by conducting whole‐sediment laboratory toxicity testing with the amphipod Hyalella azteca (for 28 d) and the midge Chironomus dilutus (for 10 d). Approximately one‐third of the sediment samples were identified as toxic for at least one test species endpoint, although concentrations of contaminants infrequently exceeded toxicity benchmarks. Ratios of contaminant concentrations relative to their benchmarks, both individually and as summed benchmark quotients, were explored on a carbon‐normalized and a dry‐weight basis. Invertebrate taxa measures from ecological surveys tended to decline with increasing urbanization and with sediment contamination. Toxicity test endpoints were more strongly related to sediment contamination than invertebrate community measures were. Sediment chemistry and sediment toxicity provided moderate and weak, respectively, explanatory power for the similarity/dissimilarity of invertebrate communities. The results indicate that current single‐chemical sediment benchmarks may underestimate the effects from mixtures of sediment contaminants experienced by lotic invertebrates. 

","language":"English","publisher":"Wiley","doi":"10.1002/etc.4705","usgsCitation":"Moran, P.W., Kemble, N.E., Waite, I.R., Mahler, B., Nowell, L.H., and Van Metre, P.C., 2020, Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams: Environmental Toxicology and Chemistry, v. 39, no. 6, p. 1219-1232, https://doi.org/10.1002/etc.4705.","productDescription":"14 p.","startPage":"1219","endPage":"1232","ipdsId":"IP-109079","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":375804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, South Carolina, North Carolina, Viriginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.9130859375,\n 32.21280106801518\n ],\n [\n -82.44140625,\n 32.54681317351514\n ],\n [\n -79.5849609375,\n 34.30714385628804\n ],\n [\n -77.0361328125,\n 36.94989178681327\n ],\n [\n -76.9482421875,\n 38.75408327579141\n ],\n [\n -78.3984375,\n 38.44498466889473\n ],\n [\n -80.595703125,\n 36.70365959719456\n ],\n [\n -83.3203125,\n 34.813803317113155\n ],\n [\n -85.78125,\n 33.87041555094183\n ],\n [\n -87.5390625,\n 33.358061612778876\n ],\n [\n -87.36328125,\n 32.509761735919426\n ],\n [\n -85.9130859375,\n 32.21280106801518\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kemble, Nile E. 0000-0002-3608-0538 nkemble@usgs.gov","orcid":"https://orcid.org/0000-0002-3608-0538","contributorId":2626,"corporation":false,"usgs":true,"family":"Kemble","given":"Nile","email":"nkemble@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":791220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - 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Population persistence probability is valuable for characterizing risk to species and informing listing and conservation decisions but is challenging to estimate through traditional methods for rare, data-limited species. Modeling approaches have used citizen science data to mitigate data limitations of focal species and better estimate parameters such as occupancy and detection, but their use to estimate persistence and inform conservation decisions is limited. We developed an approach to estimate persistence using only occurrence records of the target species and citizen science occurrence data of non-target species to account for search effort and imperfect detection. We applied the approach to a highly cryptic and data-limited species, the southern hognose snake (Heterodon simus), as part of its USFWS Species Status Assessment, and estimated current (in 2018) and future persistence under plausible scenarios of varying levels of urbanization, sea level rise, and management. Of 222 known populations, 133 (60%) are likely extirpated currently (persistence probability < 50%), and 165 (74%) populations are likely to be extirpated by 2080 with no additional management. Future management scenarios that included strategies to acquire and improve habitat on currently unprotected lands with existing populations lessened the estimated rate of population declines. These results can directly inform listing decisions and conservation planning for the southern hognose snake by Federal, State, and other partners. Our approach – using occurrence records and auxiliary data from non-target species to estimate population persistence – is applicable across rare and at-risk species for evaluating extinction risk with limited data and prioritizing management actions.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108489","usgsCitation":"Crawford, B., Olds, M., Maerz, J., and Moore, C.T., 2020, Estimating population persistence for at-risk species using citizen science data: Biological Conservation, v. 243, 108489, 13 p., https://doi.org/10.1016/j.biocon.2020.108489.","productDescription":"108489, 13 p.","ipdsId":"IP-111355","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457518,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2020.108489","text":"Publisher Index Page"},{"id":395763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.34374999999999,\n 39.027718840211605\n ],\n [\n -79.98046875,\n 37.43997405227057\n ],\n [\n -83.84765625,\n 33.797408767572485\n ],\n [\n -87.5390625,\n 32.91648534731439\n ],\n [\n -90,\n 31.42866311735861\n ],\n [\n -89.82421875,\n 30.06909396443887\n ],\n [\n -87.36328125,\n 30.221101852485987\n ],\n [\n -84.375,\n 29.458731185355344\n ],\n [\n -82.705078125,\n 26.745610382199022\n ],\n [\n -80.771484375,\n 24.926294766395593\n ],\n [\n -79.27734374999999,\n 25.562265014427492\n ],\n [\n -79.89257812499999,\n 28.536274512989916\n ],\n [\n -80.5078125,\n 30.826780904779774\n ],\n [\n -78.75,\n 32.32427558887655\n ],\n [\n -75.322265625,\n 35.17380831799959\n ],\n [\n -75.41015624999999,\n 36.66841891894786\n ],\n [\n -75.673828125,\n 37.85750715625203\n ],\n [\n -76.46484375,\n 38.95940879245423\n ],\n [\n -77.34374999999999,\n 39.027718840211605\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"243","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crawford, B.A.","contributorId":275273,"corporation":false,"usgs":false,"family":"Crawford","given":"B.A.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olds, M.","contributorId":275789,"corporation":false,"usgs":false,"family":"Olds","given":"M.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maerz, J.C.","contributorId":275274,"corporation":false,"usgs":false,"family":"Maerz","given":"J.C.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":834288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211932,"text":"70211932 - 2020 - Mercury export from Arctic great rivers","interactions":[],"lastModifiedDate":"2020-08-11T21:05:02.516262","indexId":"70211932","displayToPublicDate":"2020-03-02T16:04:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Mercury export from Arctic great rivers","docAbstract":"

Land–ocean linkages are strong across the circumpolar north, where the Arctic Ocean accounts for 1% of the global ocean volume and receives more than 10% of the global river discharge. Yet estimates of Arctic riverine mercury (Hg) export constrained from direct Hg measurements remain sparse. Here, we report results from a coordinated, year-round sampling program that focused on the six major Arctic rivers to establish a contemporary (2012–2017) benchmark of riverine Hg export. We determine that the six major Arctic rivers exported an average of 20 000 kg y–1 of total Hg (THg, all forms of Hg). Upscaled to the pan-Arctic, we estimate THg flux of 37 000 kg y–1. More than 90% of THg flux occurred during peak river discharge in spring and summer. Normalizing fluxes to watershed area (yield) reveals higher THg yields in regions where greater denudation likely enhances Hg mobilization. River discharge, suspended sediment, and dissolved organic carbon predicted THg concentration with moderate fidelity, while suspended sediment and water yields predicted THg yield with high fidelity. These findings establish a benchmark in the face of rapid Arctic warming and an intensifying hydrologic cycle, which will likely accelerate Hg cycling in tandem with changing inputs from thawing permafrost and industrial activity.

","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.9b07145","usgsCitation":"Zolkos, S., Krabbenhoft, D.P., Suslova, A., Tank, S.E., McClelland, J.W., Spencer, R.G., Shiklomanov, A., Zhulidov, A.V., Gurtovaya, T., Zimov, N., Zimov, S., Mutter, E., Kutny, L., Amos, E., and Holmes, R.M., 2020, Mercury export from Arctic great rivers: Environmental Science & Technology, v. 54, no. 7, p. 4140-4148, https://doi.org/10.1021/acs.est.9b07145.","productDescription":"9 p.","startPage":"4140","endPage":"4148","ipdsId":"IP-115773","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","volume":"54","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zolkos, Scott 0000-0001-9945-6945","orcid":"https://orcid.org/0000-0001-9945-6945","contributorId":238024,"corporation":false,"usgs":false,"family":"Zolkos","given":"Scott","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":795853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suslova, Anya","contributorId":238025,"corporation":false,"usgs":false,"family":"Suslova","given":"Anya","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tank, Suzanne E. 0000-0002-5371-6577","orcid":"https://orcid.org/0000-0002-5371-6577","contributorId":238026,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":47684,"text":"Department of Biological Sciences, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":795855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClelland, James W. 0000-0001-9619-8194","orcid":"https://orcid.org/0000-0001-9619-8194","contributorId":238027,"corporation":false,"usgs":false,"family":"McClelland","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":795856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. 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M.","affiliations":[{"id":47686,"text":"Department of Earth, Ocean and Atmospheric Science, Florida State University","active":true,"usgs":false}],"preferred":false,"id":795857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shiklomanov, Alexander","contributorId":238029,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexander","affiliations":[{"id":47687,"text":"Institute for the Study of Earth, Oceans, and Space, University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":795858,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhulidov, Alexander V.","contributorId":238030,"corporation":false,"usgs":false,"family":"Zhulidov","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795859,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gurtovaya, Tatiana","contributorId":238031,"corporation":false,"usgs":false,"family":"Gurtovaya","given":"Tatiana","email":"","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795860,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimov, Nikita","contributorId":238032,"corporation":false,"usgs":false,"family":"Zimov","given":"Nikita","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795861,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimov, Sergey","contributorId":238033,"corporation":false,"usgs":false,"family":"Zimov","given":"Sergey","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795862,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mutter, Edda A.","contributorId":238034,"corporation":false,"usgs":false,"family":"Mutter","given":"Edda A.","affiliations":[{"id":47690,"text":"˚Yukon River Inter-Tribal Watershed Council, Anchorage, Alaska","active":true,"usgs":false}],"preferred":false,"id":795863,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kutny, Les","contributorId":238035,"corporation":false,"usgs":false,"family":"Kutny","given":"Les","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795864,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Amos, Edwin","contributorId":238036,"corporation":false,"usgs":false,"family":"Amos","given":"Edwin","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795865,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Holmes, Robert M.","contributorId":178901,"corporation":false,"usgs":false,"family":"Holmes","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":795866,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70211218,"text":"70211218 - 2020 - Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","interactions":[],"lastModifiedDate":"2020-07-20T12:55:11.299767","indexId":"70211218","displayToPublicDate":"2020-03-02T15:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","docAbstract":"

Part of the spatial variation in the apparent sea-level record of the last interglacial (LIG) period is due to the diverse response of coastlines to glacial isostatic adjustment (GIA) processes, particularly where coastlines were close to the Laurentide Ice Sheet during the past two glacial periods. We tested modeled LIG paleo-sea levels on New Providence Island (NPI), Bahamas and Bermuda by investigating emergent coral patch reefs and oolitic/peloidal beach deposits. Corals with closed-system histories collected from patch reefs on NPI have ages of 128-118 ka and ooids/peloids from beach ridges have closed-system ages of 128-116 ka. Elevations of patch reefs indicate a LIG paleo-sea level of at least ∼7 m to ∼9 m above present. Beach ridge sediments indicate paleo-sea levels of ∼5 m to ∼14 m (assuming subsidence, ∼7 m to ∼16 m) above present during the LIG. Some, though not all of these measurements are in good agreement with GIA models of paleo-sea level that have been simulated for the Bahamas. On Bermuda, corals with closed-system histories collected from marine deposits have ages of 126-114 ka. Although coral-bearing marine deposits on Bermuda lack the precise indication of paleo-sea level provided by patch reefs and oolitic beach ridges, these sediments nevertheless provide at least a first-order estimate of paleo-sea level. Paleo-sea level records on Bermuda are consistently lower (∼2 m to ∼7 m) than what GIA models simulate for the LIG. The reason for the reasonable agreement with models for the Bahamas and poor agreement for Bermuda is not understood, but needs further investigation in light of the probability of a higher sea level in the near future.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106212","usgsCitation":"Muhs, D., Simmons, K., Schumann, R.R., Schweig, E.S., and Rowe, M.P., 2020, Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda: Quaternary Science Reviews, v. 233, 106212, 28 p., https://doi.org/10.1016/j.quascirev.2020.106212.","productDescription":"106212, 28 p.","ipdsId":"IP-112522","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457526,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1602345","text":"Publisher Index Page"},{"id":376496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bahamas, Bermuda","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.07476806640625,\n 31.98944183792288\n ],\n [\n -64.48699951171875,\n 31.98944183792288\n ],\n [\n -64.48699951171875,\n 32.55838861348271\n ],\n [\n -65.07476806640625,\n 32.55838861348271\n ],\n [\n -65.07476806640625,\n 31.98944183792288\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.21142578125,\n 23.46324633155036\n ],\n [\n -75.21240234375,\n 23.46324633155036\n ],\n [\n -75.21240234375,\n 27.196014383173306\n ],\n [\n -79.21142578125,\n 27.196014383173306\n ],\n [\n -79.21142578125,\n 23.46324633155036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"233","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Kathleen R. 0000-0002-7920-094X","orcid":"https://orcid.org/0000-0002-7920-094X","contributorId":229460,"corporation":false,"usgs":false,"family":"Simmons","given":"Kathleen R.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":793242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schumann, R. Randall 0000-0001-8158-6960 rschumann@usgs.gov","orcid":"https://orcid.org/0000-0001-8158-6960","contributorId":1569,"corporation":false,"usgs":true,"family":"Schumann","given":"R.","email":"rschumann@usgs.gov","middleInitial":"Randall","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schweig, Eugene S. III 0000-0003-3669-9741","orcid":"https://orcid.org/0000-0003-3669-9741","contributorId":229461,"corporation":false,"usgs":false,"family":"Schweig","given":"Eugene","suffix":"III","email":"","middleInitial":"S.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":793244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowe, Mark P.","contributorId":229462,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark","email":"","middleInitial":"P.","affiliations":[{"id":41653,"text":"Bermuda","active":true,"usgs":false}],"preferred":false,"id":793245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208261,"text":"ofr20201001 - 2020 - Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2022-04-21T20:26:12.784524","indexId":"ofr20201001","displayToPublicDate":"2020-03-02T08:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1001","displayTitle":"Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama","title":"Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama","docAbstract":"

Forecasting barrier island evolution provides coastal managers and stakeholders the ability to assess the resiliency of these important coastal environments that are home to both established communities and existing natural habitats. This study uses an established coupled model framework to assess how Dauphin Island, Alabama, responds to various storm and sea-level change scenarios, along with a suite of restoration measures, over the course of a decade. The coupled model framework uses validated models for long-term alongshore sediment transport (Delft 3D), short-term storm induced impacts (XBeach), as well as dune building and recovery (empirical dune growth model). This model framework was simulated with the various storm and sea-level change scenarios on a non-restored Dauphin Island, then a subset of the storm and sea-level change scenarios were applied to a suite of seven different restoration measures to determine how they would influence the morphologic evolution over a decadal period. Topographic and bathymetric changes captured in post-simulation digital elevation models were then passed on to partners for various simulations to determine the effects on habitat evolution and water quality as it relates to oyster reef and submerged aquatic vegetation.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, The Water Institute of the Gulf, and the University of North Carolina at Wilmington","usgsCitation":"Mickey, R.C., Godsey, E., Dalyander, P.S., Gonzalez, V., Jenkins, R.L., III, Long, J.W., Thompson, D.M., and Plant, N.G., 2020, Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1001, 45 p., https://doi.org/10.3133/ofr20201001.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113301","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399440,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109734.htm"},{"id":372467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2020/1001/ofr20201001.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1001"},{"id":372442,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDM1OJ","text":"USGS data release","linkHelpText":"Dauphin Island decadal forecast evolution model inputs and results"},{"id":372303,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191139","text":"Open-File Report 2019-1139","linkHelpText":"- Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution"},{"id":372301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2020/1001/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3355712890625,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.180747605060766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-03-02","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Godsey, Elizabeth 0000-0003-4621-7857","orcid":"https://orcid.org/0000-0003-4621-7857","contributorId":222094,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","affiliations":[{"id":34200,"text":"Army Corp of Engineers","active":true,"usgs":false}],"preferred":false,"id":781176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":222095,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy ","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":781177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzalez, Victor 0000-0003-1463-4891","orcid":"https://orcid.org/0000-0003-1463-4891","contributorId":222096,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Victor","email":"","affiliations":[{"id":34200,"text":"Army Corp of Engineers","active":true,"usgs":false}],"preferred":false,"id":781178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":781181,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781180,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":781182,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208373,"text":"ofr20191139 - 2020 - Development of a modeling framework for predicting decadal barrier island evolution","interactions":[],"lastModifiedDate":"2022-04-21T19:50:07.768456","indexId":"ofr20191139","displayToPublicDate":"2020-03-02T08:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1139","displayTitle":"Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution","title":"Development of a modeling framework for predicting decadal barrier island evolution","docAbstract":"

Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191139","usgsCitation":"Mickey, R.C., Long, J.W., Dalyander, P.S., Jenkins, R.L., III, Thompson, D.M., Passeri, D.L., and Plant, N.G., 2019, Development of a modeling framework for predicting decadal barrier island evolution: U.S. Geological Survey Open-File Report 2019–1139, 46 p., https://doi.org/10.3133/ofr20191139.","productDescription":"Report: vi, 46 p.; Data Release","ipdsId":"IP-111247","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399428,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109735.htm"},{"id":372441,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ALL6C","text":"USGS data release","linkHelpText":"Dauphin Island decadal hindcast model inputs and results"},{"id":372678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2019/1139/ofr20191139.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1139"},{"id":372308,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201001","text":"Open-File Report 2020-1001","linkHelpText":"- Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama"},{"id":372306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2019/1139/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37677001953125,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.18905718468536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-03-02","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":781647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. 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Ogden Dunes, Indiana, and the nearby Indiana Dunes National Lakeshore observed shoreline erosion due to engineered structures associated with Burns Waterway Harbor, east of Ogden Dunes, impeding natural east to west sediment migration. To remedy this, USACE placed over 450,000 cubic meters, or m³, of dredged material post 2006 in the nearshore of Ogden Dunes. However, the effectiveness of nearshore placements for shoreline protection and littoral nourishment is not fully established. To improve nearshore placement effectiveness, USACE monitored the June/July 2016 placement and subsequent movement of 107,000 m³ of dredged material in the nearshore region at Ogden Dunes. This involved an extensive monitoring scheme of three bathymetry surveys, and two acoustic Doppler current profiler deployments, a Coastal Modeling System numerical model of the changes following placement, and a prediction of sediment transport direction using the Sediment Mobility Tool. The SMT predicted sediment migration direction was compared to observations. Observations indicated that between 10/11/2016 and 11/15/2016 the centroid of the sediment above the pre-placement survey moved 17 m onshore. These observations agreed with SMT predictions onshore migration under storm and typical wave conditions. CMS accurately reproduced the hydrodynamic features.","language":"English","publisher":"U.S. Coastal and Hydraulics Laboratory, U.S. Engineer Research and Development Center ","doi":"10.21079/11681/35853","collaboration":"USACE ERDC-CHL\nUSACE Chicago District","usgsCitation":"Young, D.L., Brutsche, K.E., Li, H., McFall, B.C., Maloney, E., McClain, K.E., Bucaro, D.F., LeRoy, J.Z., Duncker, J.J., Johnson, K.K., and Jackson, P.R., 2020, Analysis of nearshore placement of sediments at Ogden Dunes, Indiana, ix, 85 p., https://doi.org/10.21079/11681/35853.","productDescription":"ix, 85 p.","ipdsId":"IP-104560","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":457533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21079/11681/35853","text":"Publisher Index 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johnsonk@usgs.gov","orcid":"https://orcid.org/0000-0003-2703-5994","contributorId":4220,"corporation":false,"usgs":true,"family":"Johnson","given":"Kevin","email":"johnsonk@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785006,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785007,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70208998,"text":"70208998 - 2020 - USGS-Water Resources Mission Area progress toward an internet of water","interactions":[],"lastModifiedDate":"2020-03-11T06:36:22","indexId":"70208998","displayToPublicDate":"2020-03-02T06:36:06","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"USGS-Water Resources Mission Area progress toward an internet of water","docAbstract":"

No abstract available.

","language":"English","publisher":"AWRA","usgsCitation":"Blodgett, D.L., and Read, E., 2020, USGS-Water Resources Mission Area progress toward an internet of water, v. 22, no. 2, p. 11-12.","productDescription":"2 p.","startPage":"11","endPage":"12","ipdsId":"IP-116911","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":373087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373078,"type":{"id":15,"text":"Index Page"},"url":"https://www.awra.org/Members/Publications/IMPACT.aspx"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Emily 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":190110,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":784461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227806,"text":"70227806 - 2020 - Use of multiple temperature logger models can alter conclusions","interactions":[],"lastModifiedDate":"2022-02-01T20:38:40.959549","indexId":"70227806","displayToPublicDate":"2020-03-01T15:37:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Use of multiple temperature logger models can alter conclusions","docAbstract":"

Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.

","language":"English","publisher":"MDPI","doi":"10.3390/w12030668","usgsCitation":"Whittier, J.B., Westhoff, J.T., Paukert, C.P., and Rotman, R.M., 2020, Use of multiple temperature logger models can alter conclusions: Water, v. 12, no. 3, 9 p., https://doi.org/10.3390/w12030668.","productDescription":"9 p.","ipdsId":"IP-092924","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12030668","text":"Publisher Index Page"},{"id":395243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westhoff, Jacob T.","contributorId":58106,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":879,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rotman, Robin M.","contributorId":272858,"corporation":false,"usgs":false,"family":"Rotman","given":"Robin","email":"","middleInitial":"M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":832347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}